Regression Homework Help | Do My Regression Homework
Regression is a course that is studied by statistical students and indeed is an important subject whose concepts are used in data science. It is also taught in the data science course. If you lack knowledge or find it challenging to do the assignment related to regression, you can seek the help of our Regression Homework Help statisticians and data science experts who have enough knowledge and experience to work on the homework. They understand the requirements given by the professors thoroughly and will complete the task before the given deadline. The solution or write-up submitted by the team will help you secure good grades in the examination.
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What is regression?
Regression is a statistical method that is used in different industries such as finance, investment and so on to find out the relationship that is between the dependent variable and a series of other independent variables. You can use this method to understand the relationship between two or multiple variables easily. The process that is embraced to do regression analysis will let you learn the factors that can be ignored and that have a huge influence on each variable. Basically, regression has a single dependent variable and multiple independent variables. You can regress the value of a dependent variable with the independent variables. Doing this analysis will help you find out how the independent variables have an impact when there is a change in the dependent variable. There are two terms that are widely used in the regression. These include- the dependent variable and the other is the independent variable. The dependent variable will learn or forecast whereas the independent variable will provide you with the relevant information related to the relationships of variables with a target variable.
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What is regression analysis?
Regression analysis is performed for both prediction and forecasting. This field will overlap with machine learning. This type of statistical method is used in the following areas:
It helps you to learn the trends in stock prices, predict the prices and find out the risks that are in the insurance domain.
It helps you to learn the effectiveness of the marketing campaign. It can predict the pricing and sales that you can expect for a product.
It helps you to find out the relationship between variables and find out an excellent engine that can improve performance.
It will forecast to find various combinations of medicines that can help you prepare generic medicines for different diseases.
Different types of regression
There are different types of approaches that can be followed to make predictions. The technique you use will be determined by different parameters, which include a lot of independent variables, the regression line and the type of the dependent variable used.
Linear regression is the widely used approach in machine learning. The model has a predictor variable and a dependent variable that is related to each other. If there is more than one independent variable being used, then that regression is known as multiple linear regression. The dependent variable in this method is continuous. The relationship between the independent and dependent variables would be linear. It shows a linear relationship between car mileage and car displacement.
It is a technique used to fit the non-linear equation by considering the polynomial functions of an independent variable. It is considered to be a variant of the multiple linear regression model and the only difference is that the best fit line will be curvy instead of a straight line.
If there is any dependent variable that is discrete, then the logistic regression technique would come into the picture. The technique will be used for computing the probability of mutually exclusive occurrences like true or false, pass or fail, 0 or 1 and so on. The target variable will consider one to two values and the sigmoid curve will show you the connection with the independent variable. The probability of the value that you get would be between 0 to 1.
When there is multicollinearity in the data, then the ridge regression technique would be used. It is also used when the independent variables are correlated. The least-square estimates in this multicollinearity would be unbiased and the variance would diverge the observed value from that of the actual value. Ridge regression would cut down the standard errors to a greater extent by giving priority to the regression estimates.
The Lasso regression technique will penalize the magnitude of the regression coefficient. It also uses the variable selection that results in the shrinkage of the coefficient value to zero.
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|Linear Regression||Regression Equation|
|Polynomial Regression||Stepwise Regression|
|Stepwise Regression||Ridge Regression|
|Ridge Regression||Lasso Regression|
|Lasso Regression||Elastic Net Regression|
|Elastic Net Regression||Minitab|
|NCSS||Lines of regression|
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